The subject matter disclosed herein relates in general to a human collaborative robot having a triangulation-type, three-dimensional (3D) imager device, also known as a triangulation scanner.
A 3D imager uses a triangulation method to measure the 3D coordinates of points on an object. The 3D imager usually includes a projector that projects onto a surface of the object either a pattern of light in a line or a pattern of light covering an area. A camera is coupled to the projector in a fixed relationship, for example, by attaching a camera and the projector to a common frame. The light emitted from the projector is reflected off of the object surface and detected by the camera. Since the camera and projector are arranged in a fixed relationship, the distance to the object may be determined using trigonometric principles. Compared to coordinate measurement devices that use tactile probes, triangulation systems provide advantages in quickly acquiring coordinate data over a large area. As used herein, the resulting collection of 3D coordinate values or data points of the object being measured by the triangulation system is referred to as point cloud data or simply a point cloud.
Robotic devices have been widely used in manufacturing and other environments to reduce costs and improve quality. Robotic devices are hard/rigid bodies that may move in a rapid and unpredictable manner. To avoid unintended impact with human operators, a typical manufacturing cell includes a lock-out procedure whereby the robot device is disabled when human operators need to enter the area. By locking out the robotic device it is ensured that the risk of contact by a moving robot is eliminated.
One type of robotic device has been developed, referred to as a human-centric or a collaborative robot, which allows the robot and the human operator to work in close proximity to each other while minimizing the risk of impact to the human operator. These collaborative robots have been proposed and used in a variety of applications, including medical facilities, libraries and manufacturing assembly operations. Collaborative robots include sensors that allow them to monitor their surrounding area including the presence of humans. The robot's controller is programmed to receive these sensor inputs and predict the risk of impact with nearby humans. When a potential impact on a human is detected, the robot takes mitigating actions (e.g. slowing down or changing direction) to avoid contact. In manufacturing environments, these human-centric robots have found use in light assembly and small part manufacturing.
Standards, such as ISO/TS 15066 (2016) and IS 13849-1:2015 for example, have been propagated to define desired performance levels and architecture of sensing systems used with human-centric robots. These standards define operations of the systems to reduce contact risk between an operators and the robotic system. Sensing systems fall under a performance level “d” and category 3 of these standards. At this level of performance, the sensing system needs to have reliability for one type of failure as occurring once every 100-1000 years.
Accordingly, while existing triangulation-based 3D imager devices and collaborative robots are suitable for their intended purpose the need for improvement remains, particularly in providing a system for measuring objects using an imager device that cooperates with a collaborative robot.